To estimate the acceleration effect of our algorithm, we modify the backward Conv layers in Caffe [1]} framework, which is widely used in deep learning deployment.
As mentioned in Section REF , two main steps of training stage: AGBP, WGC are all based on convolution.
Most modern deep learning frameworks including Caffe convert convolution into matrix multiplication by applying the combination of im2col and col2im functions, where im2col turns a 3-D feature map tensor into a 2-D matrix for exploiting data reuse, and col2im is the inverse function of im2col. Hence, our training acceleration with sparse activation gradients can be accomplished by replacing the original matrix multiplication with sparse matrix multiplication.
